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Tests of Predictive Ability for Vector Autoregressions Used for Conditional F...
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though... -
Euromind-D: A Density Estimate of Monthly Gross Domestic Product for the Euro...
EuroMInd- D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom-up approach, pooling the density estimates of 11 GDP components, by... -
Density Forecasts With Midas Models (replication data)
We propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. First, Monte Carlo simulations... -
Out-of-Sample Return Predictability: A Quantile Combination Approach (replica...
This paper develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed... -
Have Standard VARS Remained Stable Since the Crisis? (replication data)
Small vector autoregressions are commonly used in macroeconomics for forecasting and evaluating shock transmission. This requires VAR parameters to be stable over the evaluation... -
Anchoring the yield curve using survey expectations (replication data)
The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have... -
Model selection with estimated factors and idiosyncratic components (replicat...
This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big... -
Efficient estimation of Bayesian VARMAs with time‐varying coefficients (repli...
Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general... -
Loss functions for predicted click-through rates in auctions for online adver...
We characterize the optimal loss functions for predicted click-through rates in auctions for online advertising. Whereas standard loss functions such as mean squared error or... -
Combining density forecasts using focused scoring rules (replication data)
We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual...